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Academic Growth Percentiles (SGPs) for each student are calculated relative to academic peers with similar MCAS score histories; these peers don’t need to come from the same demographic group (e.g. race/ethnicity, special education or sheltered English immersion). Instead of basing SGP calculations solely off student test scores from one year’s testing window alone, SGP calculations incorporate multiple assessments such as most recent tests in each window as well as at least one prior assessment from earlier windows.

Mean SGPs may be utilized by some federal accountability systems; however, in Wisconsin’s school and district report cards they do not serve as the main metric to measure performance; value-added growth (VAG) serves instead.

The SGP package provides classes, functions and data that enable users to conduct various analyses on student academic growth. Two of the more commonly conducted analyses include student growth projections and trajectories. To create these reports, quantile regression is utilized, placing each student’s achievement history on a normative scale and identifying those who have reached that level; additionally there are coefficient matrices included which help assess whether a target achievement target will be reached by any particular student.

To run SGP analyses, users must first prepare their data by importing relevant data sets and applying appropriate cleaning and transformation functions to it. Once this step has been completed, users can run studentGrowthPercentiles and studentGrowthProjections/trajectories functions from the SGP package; additionally there are function wrapper functions abcSGP and updateSGP which reduce the steps necessary for running these analyses, making them simpler for operational use.

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